Discounted optimal stopping problems in continuous hidden Markov models

Pavel V. Gapeev

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We study a two-dimensional discounted optimal stopping problem related to the pricing of perpetual commodity equities in a model of financial markets in which the behaviour of the underlying asset price follows a generalized geometric Brownian motion and the dynamics of the convenience yield are described by an unobservable continuous-time Markov chain with two states. It is shown that the optimal time of exercise is the first time at which the commodity spot price paid in return to the fixed coupon rate hits a lower stochastic boundary being a monotone function of the running value of the filtering estimate of the state of the chain. We rigorously prove that the optimal stopping boundary is regular for the stopping region relative to the resulting two-dimensional diffusion process and the value function is continuously differentiable with respect to the both variables. It is verified by means of a change-of-variable formula with local time on surfaces that the value function and the boundary are determined as a unique solution of the associated parabolic-type free-boundary problem. We also give a closed-form solution to the optimal stopping problem for the case of an observable Markov chain.

Keywords: Discounted optimal stopping problem; generalised geometric Brownian motion; continuous-time Markov chain; filtering estimate (Wonham filter); two-dimensional diffusion process; parabolic-type free-boundary problem; change-of-variable formula with local time on surfaces; perpetual commodity equities and defautable bonds (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2022-04-03
New Economics Papers: this item is included in nep-isf and nep-ore
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Citations: View citations in EconPapers (4)

Published in Stochastics: an International Journal of Probability and Stochastic Processes, 3, April, 2022, 94(3), pp. 335 - 364. ISSN: 1744-2508

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